Why Quantum Logistics Is More Than A Contest Between D-Wave and IonQ

Firgun Ventures is a VC firm investing in Series A/B quantum scale-ups globally.

Quantum computing in logistics is often misinterpreted as a contest between two quantum computing paradigms and two quantum companies. Logistics costs are estimated at 11% of global GDP and two of quantum's most prominent players, D-Wave (NASDAQ: QBTS) and IonQ (NYSE: IONQ), are chasing this market through different approaches. On one side sits D-Wave, whose annealers (machines built to search for low-cost combinations of yes-or-no choices) already support scheduling in live operations. On the other sits IonQ, whose gate-model machines (general-purpose quantum computers that run programmes step-by-step) are at an earlier commercial stage but can run a far wider range of algorithms, which could let them tackle harder problems as the hardware grows. Framed this way, logistics becomes a test of which architecture will prove right, and in an industry of this scale, even small improvements in vehicle use are commercially significant.

What that framing overlooks is that every logistics operation depends on two kinds of decisions, a plan made before the working day begins and a series of repairs made once the day departs from that plan. D-Wave's strongest evidence sits with the first, building recurring schedules, while IonQ's work primarily addresses the second “replanning” phase. Rather than a direct contest between the two, the better questions are what D-Wave's production record demonstrates so far, how close IonQ's research is to a live fleet, and how both companies’ approaches compare with the conventional software, quantum-inspired machines, and software firms competing to enable the same decisions.

D-Wave's Production Record Shows Quantum Can Support Everyday Planning

Planning is where quantum computing has made its clearest commercial progress, largely because recurring scheduling problems translate naturally into the form an annealer handles. Assigning drivers to shifts or cranes to container movements comes down to large numbers of yes-or-no choices, each constrained by labour rules, rest periods, and equipment availability. At Pattison Food Group, a western Canadian food and healthcare retailer, a scheduling application built on D-Wave's hybrid solver (software that divides the work between conventional processors and the annealer) cut weekly scheduling effort from 80 hours to 15. Further upstream in the logistics chain, SavantX’s system at Pier 300 in the Port of Los Angeles used terminal simulation and D-Wave technology to help raise average daily deliveries per crane from 60 to 97.

Neither result shows that quantum computing outperforms the best conventional scheduling software; Pattison's gain was measured against a largely manual process, while Pier 300’s improvement belongs to a whole system whose quantum contribution cannot be isolated. Instead, these cases demonstrate something a laboratory benchmark alone cannot establish: that D-Wave’s annealing technology can be integrated with a customer's data and operating rules to support repeated decisions in a live business. Pattison deployed it for weekly driver schedules, while SavantX says its system used D-Wave repeatedly in real time at Pier 300. That record gives D-Wave a competitive edge over other quantum hardware companies, although conventional scheduling software has long done the same and sets the benchmark to be beaten.

IonQ Tests Whether Quantum Can Recover Value When Freight Plans Break

Replanning poses a different test, and it is where IonQ and Einride, a freight technology company, have concentrated their work. When a shipment is cancelled, an electric truck can be left idle for two hours or more, and any replacement must fit the truck's location, battery charge, driver hours, and delivery windows without clashing with replacements chosen for nearby trucks. The decision also has a deadline; therefore, an answer that arrives after the truck has left is worth very little.

To study the problem, the two companies took anonymised weekly schedules from Einride's customer operations and removed shipments to imitate cancellations. A quantum algorithm proposed which replacement shipments should fill which gaps, and Einride's existing planning software used those proposals as the starting point for a full plan. Across the scenarios studied, the combined workflow delivered 1.7% more shipments on average than the conventional baseline, and over 12% more in its best scenario, with no material change in modelled operating cost. An average gain of 1.7% may sound modest, but recovering even a small share of otherwise unused fleet capacity could matter when repeated across a large operation. The demonstration figures carry important caveats, since most calculations were derived from quantum simulations run on conventional computers, with smaller problems of 25 to 35 qubits (the basic units of quantum information) run on IonQ's hardware. The method has yet to improve a live fleet plan in the time dispatchers have, and IonQ's route to building a compelling commercial product for the logistics industry depends on achieving that at larger scale.

Conventional Software and Quantum-Inspired Machines Compete for the Same Decisions

Framing the market as D-Wave against IonQ also leaves out a competing approach to the quantum methods both companies use, which is the conventional optimisation software that operators already run. A 2025 study in Scientific Reports found D-Wave's hybrid solver competitive with established solvers, on problems built from yes-or-no choices interacting in pairs, but less so as constraints were added. Würth España’s packing pilot with Telefónica, Tecnalia, and QCentroid offers a comparison closer to a buyer’s needs. The partners tested a D-Wave hybrid method against software Würth already used in production across more than 6,000 real orders and reported 3% fewer boxes and roughly 7% less space needed in delivery trucks. Telefónica also hosts a Fujitsu Digital Annealer, a conventional chip that borrows ideas from annealing, so buyers can weigh quantum, quantum-inspired, and conventional methods on the same problem.

Specialist quantum software companies, which build applications for logistics customers but do not make their own hardware, add a further layer of competition. In a proof of concept using data from 451 Amerijet flights, Uruguay’s Quantum-South identified alternative loading plans that could carry up to 30% more cargo by weight in some cases. Australia's Q-CTRL has run an Airbus supply chain problem on both IBM and IonQ machines at up to 98 qubits, projecting that the full industrial problem could be tackled by 2028. Even the hardware divide is narrowing, since D-Wave's $550 million acquisition of Quantum Circuits completed in January 2026, has accelerated its development of gate-model machines alongside its annealers. The acquisition also lets D-Wave offer both architectures, although its gate-model hardware is untested in logistics. As suppliers broaden their offerings, software firms that formulate problems and choose the processor could gain considerable influence over which hardware customers use.

Rather Than a Contest, A Multi-Layered Opportunity

Measuring the success of D-Wave and IonQ is therefore a question about two kinds of decisions. A better schedule prepared before the trucks set out has value, and so does a better replacement shipment found before an idle truck leaves the depot. D-Wave has shown that quantum methods can operate inside the first, IonQ is testing whether they can improve the second, and conventional software, quantum-inspired machines, and software intermediaries are competing for both. The useful commercial test is whether any of these methods delivers repeatable gains on specific decisions within the time an operation allows.